AB1766-HPR THE IMPACT OF FASTER DIAGNOSIS THROUGH ARTIFICIAL INTELLIGENCE ON THE QUALITY OF LIFE OF PATIENTS WITH RARE DISEASES
Authors: L. Willmen, L. Völkel, T. Willmen, A. Pankow, R. Rychlik, T. Deckersbach, S. Geyer, A. D. Wagner
Journal: Annals of the Rheumatic Diseases (2023)

<h3>Background</h3> Diseases that affect fewer than 2000 people in the general population are defined as rare diseases [1]. The rarity and the associated limited experience with these diseases, but also the large number of about 8000 rare diseases [2], pose a major diagnostic challenge for physicians. On average, affected patients need 5.6 to 7.6 years until they receive a diagnosis and thus the correct therapy [3]. These diagnostic odysseys place a heavy financial, health, and psychological burden both on patients and their families. Diagnostic decision support systems (DDSS) have great potential to assist physicians in diagnostic decision-making and can indicate the presence of a rare disease much earlier. An exploratory study conducted at the Outpatient Clinic for Rare Inflammatory Systemic Diseases with Renal Involvement at Hannover Medical School showed that the diagnostic process can be shortened by a DDSS. The median lead between diagnosis and the hypothetical time of diagnosis by a DDSS was three months [4]. A follow-up study showed that health economic savings were also possible by making the diagnosis so early [5]. <h3>Objectives</h3> Measured against the hypothetical diagnosis time points determined in the previous study [4], this study aimed to investigate, in the same patient population, the extent to which earlier diagnosis using a DDSS would have influenced patients' quality of life (QOL). <h3>Methods</h3> To determine the potential for improving QOL through artificial intelligence (AI), 71 patients from the patient cohort of the previous studies were surveyed [4][5]. The questionnaire was composed of established questionnaires such as the WHO HPQ and the SF-36. In five interview sessions from the onset of the disease, the time of professional diagnosis, to the current time, the development of QOL was retrospectively recorded. To analyze the development of QOL during the disease, but also to show the influence of the DDSS on QOL, data were also collected for the time points at which the DDSS would have indicated the correct diagnosis as the first suggestion or among the first five suggestions. Statistical analysis, graphical presentation and interpretation of the questionnaires were performed using statistical software. <h3>Results</h3> The study showed that the QOL of most patients improved with a correct diagnosis. The graphically depicted progression of the patients' health perception are shown in <b>Figure 1</b>: with the onset of the first symptoms, 65% of the patients found their state of health to be less good or bad, in course of disease, this number increased to 70%. At the current time, the time of the interviews, the number declined to 28%. Originally, no patient was in excellent health, but at the current condition after the diagnostic odyssey, 10% were able to describe their state of health as excellent. The interviews show an overall decline in well-being during the diagnostic odyssey, which slowly increases with the correct diagnosis. <h3>Conclusion</h3> The study showed how much a correct diagnosis affects the patients' QOL. The use of a DDSS would have significantly shortened the diagnostic odysseys and thus improved patients' QOL at an earlier stage. <h3>References</h3> [1]Blöß S, et al (2017) Diagnostic needs for rare diseases and shared prediagnostic phenomena: Results of a German-wide expert Delphi survey. PLoS One 12:e0172532 [2]https://www.bundesgesundheitsministerium.de/fileadmin/Dateien/3_Downloads/N/NAMSE/National_Plan_of_Action.pdf. Accessed 15 Jan 2023 [3]https://globalgenes.org/wp-content/uploads/2013/04/ShireReport-1.pdf. Accessed 15 Jan 2023 [4]Ronicke S, et al (2019) Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet J Rare Dis 14:69 [5]Willmen T, et al (2021) Health economic benefits through the use of diagnostic support systems and expert knowledge. BMC Health Serv Res 21:947 <h3>Acknowledgements:</h3> NIL. <h3>Disclosure of Interests</h3> None Declared.